Trajectory optimization using dose estimation and conflict detection

ABSTRACT

Systems and methods for radiation treatment planning can include a computing system determining an estimate of radiation dose distribution within an anatomical region of a patient, and determining a cost matrix representing an objective function, using the estimate of radiation dose distribution. The computing system can project the cost matrix on each of a plurality of fluence planes. Each of the plurality of fluence planes can be associated with a corresponding gantry-couch orientation of a plurality of gantry-couch orientations of a medical linear accelerator. The computing system can determine, using projections of the cost matrix on each of the plurality of fluence planes, a sequence of gantry-couch orientations among the plurality of gantry-couch orientations representing a treatment path.

FIELD OF THE DISCLOSURE

The present application relates generally to systems and methods forautomatic radiotherapy treatment planning. Specifically, the presentapplication relates to automatic radiotherapy treatment planning usingan estimated radiation dose distribution and detection of conflicts withrespect to, for example, organs at risk (OARs).

BACKGROUND

Radiotherapy is a radiation-based therapy that is used as a cancertreatment. Specifically, high doses of radiation are used to kill orshrink a tumor. The target region of a patient's body that is intendedto receive radiation (e.g., tumor) is referred to as the planning targetvolume (PTV). The goal is to deliver enough radiation to the PTV to killthe cancerous cells. However, other organs or anatomical regions thatare adjacent to, or surrounding, the PTV can be in the way of radiationbeams and can receive enough radiation to damage or harm such organs oranatomical regions. These organs or anatomical regions are referred toas organs at risk (OARs). Usually a physician or a radiologistidentifies both the PTV and the OARs prior to radiotherapy using, forexample, computed tomography (CT) images, magnetic resonance imaging(MRI) images, positron emission tomography (PET) images, images obtainedvia some other imaging modality, or a combination thereof. For instance,the physician or the radiologist may manually mark the PTV and/or theOARs on the medical images of the patient.

Using the medical images of the patient as well as the identified PTVand the OARs, a team of medical personnel (e.g., physicians,radiologists, oncologists, radiology technicians, other medicalpersonnel or a combination thereof), referred to herein as the treatmentplanner, determines the radiation parameters to be used during theradiotherapy treatment. These radiation parameters include, for example,the type, the angle, the radiation intensity and/or the shape of eachradiation beam. In determining these parameters, the treatment plannerattempts to achieve a radiation dose distribution to be delivered to thepatient that meets predefined criteria, e.g., set by the team. Suchcriteria usually include predefined radiation dose thresholds or rangesfor the PTV and the OARs to be met.

To optimize the radiation parameters in a way to meet the predefinedcriteria, the treatment planner usually runs a plurality of simulationswith various radiation parameters, and selects a final set of radiationparameters to be used based on the simulation results. This processusually involves tweaking the radiation parameters after eachsimulation. Such approach is time consuming, tedious and may not provideoptimal results. For instance, a patient can wait for days or weeksbefore a radiation therapy plan specific to the patient is ready.

SUMMARY

Embodiments described herein relate to an automated trajectory planningapproach for use in radiation treatment planning. Using medical imagesof the anatomy of a patient, a computing device can estimate an expectedradiation dose distribution or a typical realizable dose distributionwithin an anatomical region of a patient's body. Given an objectivefunction defined in terms of the estimated distribution and dosimetricgoals for PTV and OARs, the computing device can compute a cost matrixrepresenting the objective function defined in terms of the doseestimate, and project the cost matrix on available fluence planes. Thecomputing device can use the projections of the cost matrix to detectconflicts with predefined medical goals (e.g., related to the amount ofacceptable radiation dose in various organs of the patient), andidentify a radiation trajectory based on the identified conflicts. Foreach possible orientation of the gantry and couch of a radiationmachine, a corresponding value defined based on the correspondingprojection of the cost matrix is used as a metric to determine whetherthe orientation belongs to the final radiation trajectory.

According to one aspect, a method of radiation treatment planning caninclude one or more processors determining an estimate of radiation dosedistribution within an anatomical region of a patient. The method caninclude the one or more processors determining a cost matrixrepresenting an objective function, using the estimate of radiation dosedistribution. The objective function can be defined in terms of theestimate of radiation dose distribution and patient specific data. Themethod can include the one or more processors projecting the cost matrixon each of a plurality of fluence planes. Each of the plurality offluence planes can be associated with a corresponding gantry-couchorientation of a plurality of gantry-couch orientations of a medicallinear accelerator. The method can include the one or more processorsdetermining, using projections of the cost matrix on each of theplurality of fluence planes, a sequence of gantry-couch orientationsamong the plurality of gantry-couch orientations representing atreatment path.

In some implementations, determining the estimate of radiation dosedistribution can include determining the estimate of radiation dosedistribution as a function of a distance from a planning target volume(PTV) of the anatomical region. The objective function can reflect oneor more radiation constraints for the patient. The objective functioncan be defined to optimize an intensity modulated radiation therapy(IMRT) based radiation plan. The objective function can be defined tooptimize a volumetric modulated arc therapy (VMAT) based radiation plan.The method can include determining the plurality of gantry-couchorientations by discretizing a space of possible gantry-couchorientations. Each point of a discretized space of possible gantry-couchorientations can represent a corresponding gantry-couch orientation ofthe plurality of gantry-couch orientations.

In some implementations, projecting the cost matrix on each of aplurality of fluence planes can include applying a weighted projection.Applying the weighted projection can include weighing projected valuesof the cost matrix according to a depth relative to a planning targetvolume (PTV) inside the anatomical region in a direction of a radiationbeam. Determining the sequence of gantry-couch orientations can includecomputing, for each gantry-couch orientation, a corresponding matrix sumvalue representing a sum of a projection of the cost matrix on a targetmask of a fluence plane associated with the gantry-couch orientation,and determining the sequence of gantry-couch orientations using matrixsum values computed for the plurality of gantry-couch orientations.Determining the sequence of gantry-couch orientations can includeminimizing a total of matrix sum values over the treatment path. Thetreatment path can extend over a predefined range of gantry-couchorientations.

According to one other aspect, a radiation treatment planning system caninclude one or more processors and a memory to store computer codeinstructions. The computer code instructions, when executed, can causethe one or more processors to determine an estimate of radiation dosedistribution within an anatomical region of a patient. The one or moreprocessors can determine, using the estimate of radiation dosedistribution, a cost matrix representing an objective function. Theobjective function can be defined in terms of the estimate of radiationdose distribution and patient specific data. The one or more processorscan project the cost matrix on each of a plurality of fluence planes.Each of the plurality of fluence planes can be associated with acorresponding gantry-couch orientation of a plurality of gantry-couchorientations of a medical linear accelerator. The one or more processorscan determine, using projections of the cost matrix on each of theplurality of fluence planes, a sequence of gantry-couch orientationsamong the plurality of gantry-couch orientations representing atreatment path.

In some implementations, determining the estimate of radiation dosedistribution can include determining the estimate of radiation dosedistribution as a function of a distance from a planning target volume(PTV) of the anatomical region. The objective function can reflect oneor more radiation constraints for the patient. The objective functioncan be defined to optimize an intensity modulated radiation therapy(IMRT) based radiation plan or to optimize a volumetric modulated arctherapy (VMAT) based radiation plan. The one or more processors canfurther determine the plurality of gantry-couch orientations bydiscretizing a space of possible gantry-couch orientations. Each pointof a discretized space of possible gantry-couch orientations canrepresent a corresponding gantry-couch orientation of the plurality ofgantry-couch orientations.

In some implementations, in projecting the cost matrix on each of aplurality of fluence planes, the one or more processors can apply aweighted projection. In applying the weighted projection, the one ormore processors can weigh projected values of the cost matrix accordingto a depth relative to a planning target volume (PTV) in a direction ofa radiation beam inside the anatomical region. In determining thesequence of gantry-couch orientations, the one or more processors can(i) compute, for each gantry-couch orientation, a corresponding matrixsum value representing a sum of a projection of the cost matrix on atarget mask of a fluence plane associated with the gantry-couchorientation, and (ii) determine the sequence of gantry-couchorientations using matrix sum values computed for the plurality ofgantry-couch orientations. In determining the sequence of gantry-couchorientations, the one or more processors can minimize a total of matrixsum values over the treatment path. The treatment path can extend over apredefined range of gantry-couch orientations.

According to yet one other aspect, a computer readable medium caninclude computer code instructions stored thereon. The computer codeinstructions when executed can cause one or more processors to determinean estimate of radiation dose distribution within an anatomical regionof a patient, and determine, using the estimate of radiation dosedistribution, a cost matrix representing an objective function definedin terms of the estimate of radiation dose distribution and patientspecific data. Execution of the computer code instructions can cause theone or more processors to project the cost matrix on each of a pluralityof fluence planes. Each of the plurality of fluence planes can beassociated with a corresponding gantry-couch orientation of a pluralityof gantry-couch orientations of a medical linear accelerator. The one ormore processors can determine, using projections of the cost matrix on atarget mask of each of the plurality of fluence planes, a sequence ofgantry-couch orientations among the plurality of gantry-couchorientations representing a treatment path.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a block diagram illustrating an example computerenvironment for implementing methods and processes described herein,according to an embodiment.

FIG. 1B is a block diagram depicting one implementation of a systemarchitecture, according to an embodiment.

FIG. 2 is a flowchart illustrating an embodiment of a method ofradiation treatment planning, according to an embodiment.

FIGS. 3A-3C show simulation results associated with various steps of themethod of FIG. 2, according to an embodiment.

FIGS. 4A and 4B show images depicting a visual illustration of aprojection of a cost matrix, according to an embodiment.

FIGS. 5A and 5B show images illustrating two-dimensional (2-D) andthree-dimensional (3-D) representations of a treatment path, accordingto an embodiment.

Some or all of the figures are schematic representations for purposes ofillustration. The foregoing information and the following detaileddescription include illustrative examples of various aspects andimplementations, and provide an overview or framework for understandingthe nature and character of the claimed aspects and implementations. Thedrawings provide illustration and a further understanding of the variousaspects and implementations, and are incorporated in and constitute apart of this specification.

DETAILED DESCRIPTION

Following below are more detailed descriptions of various conceptsrelated to, and implementations of, methods, apparatuses, and systemsfor radiation treatment planning. The various concepts introduced aboveand discussed in greater detail below may be implemented in any ofnumerous ways as the described concepts are not limited to anyparticular manner of implementation. Examples of specificimplementations and applications are provided primarily for illustrativepurposes.

Radiotherapy treatment planning is a complex and patient specificoptimization problem. Given the anatomy of the patient, e.g., asillustrated in medical images of the patient, and identifications ormasks of the PTV and the OARs, the goal is to determine a treatment path(or treatment trajectory) that satisfies the criteria or constraintspredefined, for example, by physicians, radiologists or other medicalpersonnel. During radiotherapy sessions, the patient usually lies downon the couch of a radiation machine, and a gantry equipped with aradiation source rotates around the patient to deliver radiation fromdifferent angles with various intensities and/or shapes. Determining thetreatment path or trajectory includes determining a sequence ofpositions of the radiation source (e.g., relative to the patient) andcorresponding radiation angles (e.g., in the 3-D space) defining thepositions and orientations of the radiation source at which radiationbeams are emitted towards the patient. The sequence of positions of theradiation source defines a rotation path or trajectory of the gantryaround the patient. Determining the radiation path can also includedetermining, for each radiation position and angle of the sequence ofradiation positions and angles, a corresponding radiation intensityand/or beam shape.

Optimization of the radiation treatment trajectory or path leads toimprovement of dosimetric quality of a treatment plan. Specifically, thegoal of the optimization is to minimize (or maintain below acorresponding predefined upper bound value) the amount of radiation dosefor OARs while maximizing (or maintain above a corresponding predefinedlower bound value) the radiation dose for the PTV. In such a case, theradiotherapy designed according to the optimized radiation treatmenttrajectory can lead to killing the cancerous cells without damaging orharming critical organs or OARs. Trajectory optimization methods basedon manual selection and prioritization of critical organs make the taskof treatment planners difficult, are time consuming for users, requiresa trial and error procedure, and the outcome usually depends on theexperience and skill of the treatment planner.

In the current disclosure, systems and methods for improved automaticradiation treatment planning start with an estimate of expectedradiation dose distribution within an anatomical region of the patient'sbody, and identify conflicts between the estimate of the radiation dosedistribution and clinical goals for the plan. The systems and methodsdescribed herein can determine or optimize the treatment trajectory orpath by taking into account spatial regions where conflicts areexpected. The severity of the conflicts can be expressed via anobjective (or cost) function that can be evaluated at each voxel of the3-D anatomical region. The systems and methods described herein cangenerate a radiation treatment trajectory or path that avoids conflictsbased on their severity, for example, as expressed or described in thecost function.

Embodiments described herein allow for automated trajectory planning. Assuch, there's no need for a user to select or adjust weights forcritical organs in generating gantry-couch direction quality-landscapesto be used for treatment path finding. Also, the embodiments describedherein provide finer spatial precision (e.g., more accurate than perstructure) as weighting of the 3-D patient at various steps of themethods described herein can be applied at the voxel level. Forinstance, in some cases, clinical goals may call for avoiding only aspecific region of a critical organ instead of avoiding the whole organ.The finer spatial precision leads to improved final treatmenttrajectories with respect to the patient specific clinical goals or dosevolume objectives.

FIG. 1A illustrates an example computer environment 100 that can be usedto provide a network-based implementation of the methods describedherein. The computer environment 100 can include a computer server 110a, system database 110 b, a user computing device 120 and electronicdata sources 130 a-e (collectively electronic data source 130). Theabove-mentioned components may be connected to each other through anetwork 140. The examples of the network 140 may include, but are notlimited to, private or public LAN, WLAN, MAN, WAN, and the Internet. Thenetwork 140 may include both wired and wireless communications accordingto one or more standards and/or via one or more transport mediums.

The communication over the network 140 may be performed in accordancewith various communication protocols such as Transmission ControlProtocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP),and IEEE communication protocols. In one example, the network 140 mayinclude wireless communications according to Bluetooth specificationsets or another standard or proprietary wireless communication protocol.In another example, the network 140 may also include communications overa cellular network, including, e.g., a GSM (Global System for MobileCommunications), CDMA (Code Division Multiple Access), EDGE (EnhancedData for Global Evolution) network.

The computer environment 100 is not necessarily confined to thecomponents described herein and may include additional or alternatecomponents, not shown for brevity, which are to be considered within thescope of the embodiments described herein.

In some implementations, the computer server 110 a can be configured toexecute computer instructions to perform any of the methods describedherein or operations thereof. The computer server 110 a may generate anddisplay an electronic platform to display information indicative of, orrelated to, a radiation plan trajectory. The electronic platform mayinclude graphical user interface (GUI) displayed on the user computingdevice 120. An example of the electronic platform generated and hostedby the computer server 110 a may be a web-based application or a websiteconfigured to be displayed on different electronic devices, such asmobile devices, tablets, personal computer, and the like (e.g., usercomputing device 120).

The computer server 110 a may host a website accessible to end-users,where the content presented via the various webpages may be controlledbased upon each particular user's role or viewing permissions. Thecomputer server 110 a may be any computing device comprising a processorand non-transitory machine-readable storage capable of executing thevarious tasks and processes described herein. Non-limiting examples ofsuch computing devices may include workstation computers, laptopcomputers, server computers, laptop computers, and the like. While thecomputer environment 100 includes a single computer server 110 a, insome configurations, the computer server 110 a may include any number ofcomputing devices operating in a distributed computing environment.

The computer server 110 a may execute software applications configuredto display the electronic platform (e.g., host a website), which maygenerate and serve various webpages to each user computing device 120.Different users operating the user computing device(s) 120 may use thewebsite to view and/or interact with the output treatment trajectoriesor paths.

In some implementations, the computer server 110 a may be configured torequire user authentication based upon a set of user authorizationcredentials (e.g., username, password, biometrics, cryptographiccertificate, and the like). In such implementations, the computer server110 a may access the system database 110 b configured to store usercredentials, which the computer server 110 a may be configured toreference in order to determine whether a set of entered credentials(purportedly authenticating the user) match an appropriate set ofcredentials that identify and authenticate the user.

In some configurations, the computer server 110 a may generate and hostwebpages based upon a particular user's role (e.g., administrator,employee, and/or bidder). In such implementations, the user's role maybe defined by data fields and input fields in user records stored in thesystem database 110 b. The computer server 110 a may authenticate theuser and may identify the user's role by executing an access directoryprotocol (e.g. LDAP). The computer server 110 a may generate webpagecontent that is customized according to the user's role defined by theuser record in the system database 110 b.

In some embodiments, the computer server 110 a receives medical images,masks and/or medical data indicative of medical goals from a user (orretrieve from a data repository), process the data, and displays anindication of the treatment trajectory on the electronic platform. Forinstance, in a non-limiting example, a user operating the computingdevice 130 a uploads a series of images of a CT scan or other medicalimages using the electronic platform. The computer server 110 a candetermine the treatment trajectory based on input data, and display theresults via the electronic platform on the user computing device 120 orthe computing device 130 a. The user computing device 120 and/or thecomputing device 130 a may be any computing device comprising aprocessor and a non-transitory machine-readable storage medium capableof performing the various tasks and processes described herein.Non-limiting examples of a network node may be a workstation computer,laptop computer, tablet computer, and server computer. In operation,various users may use user computing devices 120 and or computing device130 a to access the GUI operationally managed by the computer server 110a.

The electronic data sources 130 may represent various electronic datasources that contain and/or retrieve medical images of patients. Forinstance, database 130 b and third-party server 130 c may represent datasources providing the corpus of data (e.g., medical images, masks orother medical data) needed for the computer server 110 a to determinetreatment trajectories. The computer server 110 a may also retrieve thedata directly from a medical scanner 130 e and/or medical imaging device130 d (e.g., CT scan machine).

In some implementations, the methods described herein or operationsthereof can be implemented by the user device 120, any of the electronicdevices 130 or a combination thereof.

While FIG. 1A shows a network based implementation, it is to be notedthat methods described herein can be implemented by a single computingdevice that receives the medical images and medical data for a patientand determines a radiation treatment trajectory or path according tomethods described herein.

Referring to FIG. 1B, a block diagram depicting one implementation of asystem architecture for a computing system 150 that may be employed toimplement methods described herein is shown, according to inventiveconcepts of the current disclosure. The computing system 150 can includea computing device 152. The computing device 152 can represent anexample implementation of any of the devices 110 a, 120 and/or 130 a-eof FIG. 1A. For instance, the computing device 152 can include, but isnot limited to, a computed tomography (CT) scanner, a medical linearaccelerator device, a desktop, a laptop, a hardware computer server, aworkstation, a personal digital assistant, a mobile computing device, asmart phone, a tablet, or other type of computing device. The computingdevice 152 can include a one or more processors 154 to execute computercode instructions, a memory 156 and a bus 158 communicatively couplingthe processor 154 and the memory 156.

The one or more processors 154 can include a microprocessor, a generalpurpose processor, a multi-core processor, a digital signal processor(DSP) or a field programmable gate array (FPGA), an application-specificintegrated circuit (ASIC) or other type of processor. The one or moreprocessors 154 can be communicatively coupled to the bus 158 forprocessing information. The memory 156 can include a main memory device160, such as a random-access memory (RAM) other dynamic storage device,coupled to the bus 158 for storing information and instructions to beexecuted by the processor 154. The main memory device 160 can be usedfor storing temporary variables or other intermediate information duringexecution of instructions (e.g., related to methods described hereinsuch as method 200) by the processor 154. The computing device 152 caninclude a read-only memory (ROM) 162 or other static storage devicecoupled to the bus 158 for storing static information and instructionsfor the processor 154. For instance, the ROM 162 can store medicalimages of patients, for example, received as input. The ROM 162 canstore computer code instructions related to, or representing animplementation of, methods described herein. A storage device 164, suchas a solid state device, magnetic disk or optical disk, can be coupledto the bus 158 for storing (or providing as input) information and/orinstructions.

The computing device 152 can be communicatively coupled to, or caninclude, an input device 166 and/or an output device 168. The computingdevice 102 can be coupled via the bus 158 to the output device 168. Theoutput device 168 can include a display device, such as a Liquid CrystalDisplay (LCD), Thin-Film-Transistor LCD (TFT), an Organic Light EmittingDiode (OLED) display, LED display, Electronic Paper display, PlasmaDisplay Panel (PDP), or other display, etc., for displaying informationto a user. The output device 168 can include a communication interfacefor communicating information to other external devices. An input device166, such as a keyboard including alphanumeric and other keys, may becoupled to the bus 158 for communicating information and commandselections to the processor 154. In another implementation, the inputdevice 166 may be integrated within a display device, such as in a touchscreen display. The input device 166 can include a cursor control, suchas a mouse, a trackball, or cursor direction keys, for communicatingdirection information and command selections to the processor 154 andfor controlling cursor movement on the display device.

According to various implementations, the methods described herein orrespective operations can be implemented as an arrangement of computercode instructions that are executed by the processor(s) 154 of thecomputing system 150. The arrangement of computer code instructions canbe read into main memory device 160 from another computer-readablemedium, such as the ROM 162 or the storage device 164. Execution of thearrangement of computer code instructions stored in main memory device160 can cause the computing system 150 to perform the methods describedherein or operations thereof. In some implementations, one or moreprocessors 154 in a multi-processor arrangement may be employed toexecute the computer code instructions representing an implementation ofmethods or processes described herein. In some other implementations,hard-wired circuitry may be used in place of or in combination withsoftware instructions to effect illustrative implementation of themethods described herein or operations thereof. In general,implementations are not limited to any specific combination of hardwarecircuitry and software. The functional operations described in thisspecification can be implemented in other types of digital electroniccircuitry, in computer software, firmware, hardware or a combinationthereof.

FIG. 2 shows a flowchart illustrating an embodiment of a method 200 ofradiation treatment planning, according to inventive concepts of thisdisclosure. The method 200 can include the computing system 150 ordevice determining an estimate of radiation dose distribution within apatient's body (STEP 202), and determining a cost matrix representing anobjective function (STEP 204). The method 200 can include the computingsystem 150 or computing device 152 projecting the cost matrix on each ofa plurality of fluence planes or corresponding masks (STEP 206), anddetermining a treatment path based on projections of the cost matrix onthe plurality of fluence planes or corresponding masks (STEP 208).

Referring back to FIGS. 1B and 2, the method 200 can include thecomputing system 150 or computing device 152 determining an estimate ofradiation dose distribution within a patient's body (STEP 202). Thecomputing system 150 can obtain medical images of an anatomy region of apatient, one or more structure masks of the PTV and OARs, informationindicative of clinical goals, or a combination thereof. A CT scanner, anMM device, an ultrasound imaging device, a medical imaging device ofother type or a combination thereof can generate the medical images ofthe patient. The medical images can include 3-D images, 2-D images or acombination thereof. Obtaining the one or more masks can includereceiving the masks from another computing device. In some otherimplementations, the computing system 150 can segment one or moremedical images of the patient, and generate the mask(s) using thesegmented images. A user can tag segmented regions of the medical imagesas corresponding to the PTV or OARs. The information indicative ofclinical goals can include, for each of the PTV and OARs, acorresponding radiation dose threshold, corresponding radiation doserange or corresponding desired radiation dose value. In someimplementations, the radiation dose thresholds, ranges or desired valuescan be defined in connection with the one or more structure masks. Thecomputing system 150 can receive the information indicative of clinicalgoals as input via the input device 166.

The estimate of radiation dose distribution can represent an expectedradiation dose distribution, or a typical realizable dose distribution,within the anatomical region of the patient's body, responsive to theradiotherapy to be performed. The dose distribution estimate does notnecessarily have to be the optimal radiation dose distribution. In someimplementation, the computing system 150 or the processor 154 cangenerate the estimate of the radiation dose distribution as a functionof distance from the PTV to model the usual falloff of the radiationdose around the PTV. The estimation can be isotropic to all directionsfrom the PTV. In some implementations, the computing system 200 cangenerate the estimate of the radiation dose distribution as:

$\begin{matrix}{{{D(x)} = {\alpha\frac{d_{0}}{{d(x)} + d_{0}}}},} & (1)\end{matrix}$

where d₀ is constant and d represents a distance from the surface of thePTV. The variable x represents a point or voxel in the 3-D space, and arepresents a coefficient that can be equal to, or defined relative to,the prescribed dose of the PTV. The computing system 150 can generatethe estimate of the radiation dose distribution using some otherfunction defined in terms of the distance d(x).

Referring to FIG. 3A, a 2-D slice of an example estimate of theradiation dose distribution with an anatomical region 302 is shown. Theanatomical region 302 includes the PTV 304 and two OARs 306 and 308. Theestimate of the radiation dose distribution shown in FIG. 3A is definedas D(x), as in equation (1). As illustrated in FIG. 3A, the radiationdose function D(x) decreases significantly outside the PTV 304.

In the case where the PTV includes a plurality of disjoint regions(e.g., a plurality of tumors or anomalies), the computing system 150 cangenerate or define the estimate of the radiation dose distribution interms of the distance to each of the various PTV regions. For

$\alpha\frac{d_{0}}{{d(x)} + d_{0}}$

instance, at each voxel x, the computing system 150 can evaluate theexpression (or some other function of distance) for various distances todifferent PTV regions and use the maximum value as the radiation doseD(x) if the voxel x is outside any PTV region. If the voxel x is insidea PTV region, the computing system 150 or processor 154 can use themaximum value of the evaluated function of distance as the radiationdose D(x).

Referring back to FIGS. 1B and 2, the method 200 can include thecomputing system 150 or processor(s) 154 determining a cost matrixrepresenting an objective function (STEP 204). The objective functioncan be defined in terms of the estimate of radiation dose distributionD(x) and patient specific data, such as dosimetric goals for PTV andOARs. In some implementations, the computing system 150 or processor(s)154 can define the objective function as:

Φ(x)=W(x)(D(x)−C(x))².  (2)

The objective function Φ(x) is defined at each voxel x as the squaredifference between the estimated radiation dose D(x) and a desired orreference radiation dose C(x) multiplied by a weighting value W(x). Thereference radiation dose function C(x) can be defined, within eachstructure (e.g., PTV or OAR) of the anatomical region, to be equal to acorresponding constant dose value or threshold. The function C(x) canreflect dosimetric goals for PTV and OARs specific to the patient. Forinstance the function C(x) can be equal to a first radiation dose valuewithin the PTV 304, equal to a second radiation dose value within theOAR 306, and equal to a third radiation dose value within the OAR 308.The first, second and third radiation dose values can be defined basedon clinical or dosimetric goals specific to the patient. The weightingfunction W can reflect the severity of deviating from the radiationfunction C(x). In OARs, Φ(x) can be defined to be zero where D(x)<C(x).The computing system 150 or processor(s) 154 can determine the value ofvoxel x of the cost matrix as Φ(x). Referring to FIG. 3B, a 2-D slice ofan example cost matrix is shown. The cost matrix voxel values, or thecorresponding objective function values, are calculated based on theobjective function and estimated radiation dose distribution D(x) ofFIG. 3A.

In some implementations, the cost matrix can be defined to represent thederivative ∂_(D) Φ of the objective function Φ(x) with respect to theradiation dose, or represent the absolute value of the derivative ∂_(D)Φ. The computing system 150 or processor(s) 154 can define or computethe voxel value, at each voxel x of cost matrix, as the derivative∂_(D(x))Φ(x) of the objective function Φ(x) with respect to theradiation dose, or the corresponding absolute value |∂_(D(x))Φ(x)|. Insome implementations, the cost matrix can be defined differently. Forinstance, the computing system 150 or processor(s) 154 can define orcompute the cost matrix as another function, e.g., other than theabsolute value of the derivative, of the objective function Φ(x).

The method 200 can include the computing system 150 or processor(s) 154projecting the cost matrix on each of a plurality of fluence planes(STEP 206). The computing system 150 or the processor(s) 154 candiscretize a space of possible gantry-couch orientations. Each point ofthe discretized space of possible gantry-couch orientations canrepresent a corresponding gantry-couch orientation, e.g., (gantry angle,couch angle) pair, of the plurality of gantry-couch orientations. Forinstance, the space of possible gantry-couch orientations can be a 2-Dspace with the x-axis representing available gantry angles and they-axis representing available couch angles or vice versa. That is,assuming that both the gantry and the couch are capable of moving orrotating, each relative orientation or position of the gantry and thecouch can be expressed in terms of a corresponding gantry angle and acorresponding couch angle. Each of the gantry angle and couch angle canbe defined in the 3-D space relative to corresponding referencedirections. Each (gantry angle, couch angle) pair can define acorresponding position and/or orientation of the couch or the patient,and a corresponding position and/or orientation of the gantry or acorresponding direction of the radiated beam.

For each (gantry angle, couch angle) pair, the computing system 150 orprocessor(s) 154 can compute a projection of the cost matrix on acorresponding fluence plane. The computing system 150 or processor(s)154 can project the voxels of the cost matrix along the correspondingradiation beam direction on the corresponding fluence plane. A voxel ofa cost matrix is projected by determining the pixel at the fluence planethat is intersected by a ray that goes through the voxel in thedirection of the radiation beam. The cost value of the voxel is added tothe pixel value at the fluence plane.

In some implementations, the computing system 150 or the processor(s)154 can apply a weighting to each of the projections of the cost matrix.For each projection of the cost matrix, the computing system 150 or theprocessor(s) 104 can apply corresponding weighted function defined interms of a depth relative to a planning target volume (PTV) inside theanatomical region in a direction of a radiation beam. Applying theweighted projection can include weighing projected values of the costmatrix according to a depth relative to a planning target volume (PTV)inside the anatomical region in a direction of a radiation beam. Theweight of projection from inside PTV can be assumed zero in order toinclude cost contributions only from OARs in the projections. Forinstance, the computing system 150 or the processor(s) 154 can applyhigher weights to the volume before the PTV (considering the directionof the corresponding radiation beam) than the volume behind or after thePTV. Weight of PTV can be assumed to be equal to zero in order toinclude cost contributions only from OARs, or from all normal tissueincluding OARs, in the projections.

The method 200 can include the computing system 150 or the processor(s)154 determining a treatment path based on projections of the cost matrixon the target masks of the plurality of fluence planes (STEP 208). Thecomputing system 150 or the processor(s) 154 can compute for each(gantry angle, couch angle) pair a corresponding aggregate projectionvalue representing the sum of the entries of the correspondingprojection matrix. That is, for each (gantry angle, couch angle) pair,the computing system 150 or the processor(s) 154 can compute the sum ofentries of the corresponding projection of the cost matrix to determinethe corresponding aggregate projection value. In some implementations, asum of entries is computed over a target mask of a fluence plane. Atarget mask can be formed by projecting voxels of a PTV to the fluenceplane. The pixels receiving any projection are included in the targetmask. Some margin around the target projection may be included in themask. That is, projection of the cost matrix can be on the whole fluenceplane, but only the part of the projection that hits the target mask isrelevant for determining aggregate sums and thus a treatment path. Theaggregate projection values corresponding to the (gantry angle, couchangle) pairs represent a measure of the severity of conflicts with themedical or clinical criteria or constraints. For a given (gantry angle,couch angle) pair, the corresponding aggregate projection value isindicative of whether a beam radiated by the gantry at the gantry angleand while the couch is oriented according to the couch angle violatesany of the clinical or medical criteria set by the medical staff takingcare of the patient. The larger the aggregate projection value, the moresevere is the conflict associated with the corresponding (gantry angle,couch angle) pair.

The computing system 150 or the processor(s) 154 can use the computedaggregate projection values to determine the optimal treatment path ortrajectory. Specifically, the computing system 150 or the processor(s)154 can apply a path or trajectory search to a matrix of aggregateprojection values to determine the optimal treatment path or trajectory.The columns of the matrix of aggregate projection values can correspondto different gantry angles and the rows can correspond to differentcouch angles, or vice versa. In performing the path search, thecomputing system 150 or the processor(s) 154 can start from an initialentry of the matrix of aggregate projection values and proceediteratively to determine a sequence of entries until reaching a finalentry. The computing system 150 or the processor(s) 154 can apply thepath search in a way to minimize the corresponding total severity or thecorresponding sum of aggregate projection values. For instance, thecomputing system 150 or the processor(s) 154 can apply a path searchalgorithm, such as the A* algorithm, to determine the path or trajectoryhaving the smallest sum of aggregate projection values.

Each entry of the sequence of determined entries of the matrix ofaggregate projection values represents a corresponding (gantry angle,couch angle) pair. As such, determining a sequence of entries of thematrix of aggregate projection values implies determining a sequence of(gantry angle, couch angle) pairs that form or represent the treatmentpath or trajectory. The input to the path search algorithm can include astarting point and an end point of the path. In some implementations,the starting point and the end point can be the same so that the path ortrajectory forms a full loop around the patient. The computing system150 or the processor(s) 154 can select the starting point as the (gantryangle, couch angle) pair corresponding to the smallest entry of thematrix of aggregate projection values. In some implementations, thecomputing system 150 or the processor(s) 154 can select the startingpoint differently.

Referring to FIG. 3C, an image of an example matrix of aggregateprojection values 310 representing the severity of conflicts associatedwith possible (gantry angle, couch angle) pairs is shown together with atreatment path 312 that is determined based on the matrix of aggregateprojection values 310. The treatment path 312 represents a sequence of(gantry angle, couch angle) pairs that define a loop around the patient.The gantry angles are equally spaced. The treatment path shown isassociated with the minimum sum of corresponding entries of the matrixof aggregate projection values 310. The flat gray regions are forbiddeneither by those (gantry angle, couch angle) directions causing acollision or by the beam entering to the patient volume through clippingplanes of the CT image. Points a and b in the aggregate projectionmatrix correspond to the projection directions a and b in FIG. 3B,respectively.

FIG. 4A shows an image depicting an example arrangement of varioushypothetical anatomical structures. Structure 402 represents a PTV,while structures 404 and 406 represent two distinct OARs. FIG. 4B showsan image depicting a visual illustration of a projection of a costmatrix at the beam orientation corresponding to FIG. 4A. In this case,the OAR structure 406 is assumed to have clinical goals that are highlyconflicting with the estimated radiation dose, therefore, producing highintensities in the projection of the cost matrix of FIG. 4B. Incontrast, the OAR structure 404 is assumed to have clinical goals thatare less conflicting with the estimated radiation dose, therefore,producing relatively low intensities in the projection of the costmatrix of FIG. 4B.

FIGS. 5A and 5B show images illustrating two-dimensional (2-D) andthree-dimensional (3-D) representations of a treatment path, accordingto inventive concepts of this disclosure. FIG. 5A shows another exampleof a matrix of aggregate projection values and an optimal path 504 thatminimizes the sum of corresponding entries of the aggregate projectionmatrix. The flat gray regions are forbidden either by those (gantryangle, couch angle) directions causing a collision or by the beamentering to the patient volume through clipping planes of the CT image.FIG. 5B shows the optimal path 502 in the 3-D space. FIG. 5B alsoillustrates a radiation beam at one point of the optimal path.

The computing system 150 or the processor(s) 154 can employ the method200 to optimize an intensity modulated radiation therapy (IMRT) basedradiation plan or optimize a volumetric modulated arc therapy (VMAT)based radiation plan. For instance, the objective function can bedefined to optimize an IMRT based radiation plan or to optimize a VMATbased radiation plan. In VMAT, a Multi Leaf Collimator (MLC) that ismounted on the head of the gantry is used to shape the radiation beam.The MLC includes a set of metal leaves that move in-and-out and blockparts of the radiation to modulate the beam and make the radiation moreconformal to the PTV shape. In VMAT, the gantry can deliver theradiation continuously while moving around the patient, while the MLCmay block the radiation at some portions of the path. As such, thetreatment path optimization in VMAT may involve determining segments ofthe path during which the MLC blocks the radiation. In IMRT, the gantrystops at few angles (e.g., about 5 to 10 angles) and delivers theradiation by modulating the beams. As such, the path optimization caninclude determining the (gantry angle, couch angle) pairs at which thegantry stops to deliver radiation to the patient.

One should note that the examples discussed in this specification areprovided for illustrative purposes and re not to be interpreted aslimiting. For example, the estimate of the radiation dose distributioncan defined using other functions different from the function D(x)described in equation (1). Also, the computing system 150 can initiatethe path search algorithm in various different ways.

Each method described in this disclosure can be carried out by computercode instructions stored on computer-readable medium. The computer codeinstructions, when executed by one or more processors of a computingdevice, can cause the computing device to perform that method.

While the disclosure has been particularly shown and described withreference to specific embodiments, it should be understood by thoseskilled in the art that various changes in form and detail may be madetherein without departing from the spirit and scope of the inventiondescribed in this disclosure.

While this disclosure contains many specific embodiment details, theseshould not be construed as limitations on the scope of any inventions orof what may be claimed, but rather as descriptions of features specificto particular embodiments of particular inventions. Certain featuresdescribed in this specification in the context of separate embodimentscan also be implemented in combination in a single embodiment.Conversely, various features described in the context of a singleembodiment can also be implemented in multiple embodiments separately orin any suitable subcombination. Moreover, although features may bedescribed above as acting in certain combinations and even initiallyclaimed as such, one or more features from a claimed combination can insome cases be excised from the combination, and the claimed combinationmay be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated in a single software product or packaged intomultiple software products.

References to “or” may be construed as inclusive so that any termsdescribed using “or” may indicate any of a single, more than one, andall of the described terms.

Thus, particular embodiments of the subject matter have been described.Other embodiments are within the scope of the following claims. In somecases, the actions recited in the claims can be performed in a differentorder and still achieve desirable results. In addition, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain embodiments, multitasking and parallel processingmay be advantageous.

What is claimed is:
 1. A method of radiation treatment planningcomprising: determining, by one or more processors, an estimate ofradiation dose distribution within an anatomical region of a patient;determining, by the one or more processors using the estimate ofradiation dose distribution, a cost matrix representing an objectivefunction defined in terms of the estimate of radiation dose distributionand patient specific data; projecting, by the one or more processors,the cost matrix on each of a plurality of fluence planes, each of theplurality of fluence planes associated with a corresponding gantry-couchorientation of a plurality of gantry-couch orientations of a medicallinear accelerator device; and determining, using projections of thecost matrix on each of the plurality of fluence planes, a sequence ofgantry-couch orientations among the plurality of gantry-couchorientations representing a treatment path.
 2. The method of claim 1,wherein determining the estimate of radiation dose distribution includesdetermining the estimate of radiation dose distribution as a function ofa distance from a planning target volume (PTV) of the anatomical region.3. The method of claim 1, wherein the objective function reflects one ormore radiation constraints for the patient.
 4. The method of claim 1,wherein the objective function is defined to optimize an intensitymodulated radiation therapy (IMRT) based radiation plan.
 5. The methodof claim 1, wherein the objective function is defined to optimize avolumetric modulated arc therapy (VMAT) based radiation plan.
 6. Themethod of claim 1 further comprising: determining the plurality ofgantry-couch orientations by discretizing a space of possiblegantry-couch orientations, each point of a discretized space of possiblegantry-couch orientations represents a corresponding gantry-couchorientation of the plurality of gantry-couch orientations.
 7. The methodof claim 1, wherein projecting the cost matrix on each of a plurality offluence planes includes applying a weighted projection.
 8. The method ofclaim 7, wherein applying the weighted projection includes weighingprojected values of the cost matrix according to a depth relative to aplanning target volume (PTV) inside the anatomical region in a directionof a radiation beam.
 9. The method of claim 1, wherein determining thesequence of gantry-couch orientations includes: computing, for eachgantry-couch orientation, a corresponding matrix sum value representinga sum of a projection of the cost matrix on a target mask of a fluenceplane associated with the gantry-couch orientation; and determining thesequence of gantry-couch orientations using matrix sum values computedfor the plurality of gantry-couch orientations.
 10. The method of claim9, wherein determining the sequence of gantry-couch orientationsincludes minimizing a total of matrix sum values over the treatmentpath, the treatment path extending over a predefined range ofgantry-couch orientations.
 11. A radiation treatment planning systemcomprising: one or more processors; and a non-transitory memory to storecomputer code instructions, the computer code instructions when executedcause the one or more processors to: determine an estimate of radiationdose distribution within an anatomical region of a patient; determine,using the estimate of radiation dose distribution, a cost matrixrepresenting an objective function defined in terms of the estimate ofradiation dose distribution and patient specific data; project the costmatrix on each of a plurality of fluence planes, each of the pluralityof fluence planes associated with a corresponding gantry-couchorientation of a plurality of gantry-couch orientations of a medicallinear accelerator device; and determine, using projections of the costmatrix on each of the plurality of fluence planes, a sequence ofgantry-couch orientations among the plurality of gantry-couchorientations representing a treatment path.
 12. The radiation treatmentplanning system of claim 11, wherein determining the estimate ofradiation dose distribution includes determining the estimate ofradiation dose distribution as a function of a distance from a planningtarget volume (PTV) of the anatomical region.
 13. The radiationtreatment planning system of claim 11, wherein the objective functionreflects one or more radiation constraints for the patient.
 14. Theradiation treatment planning system of claim 11, wherein the objectivefunction is defined to optimize an intensity modulated radiation therapy(IMRT) based radiation plan or to optimize a volumetric modulated arctherapy (VMAT) based radiation plan.
 15. The radiation treatmentplanning system of claim 11, wherein the one or more processors arefurther configured to: determine the plurality of gantry-couchorientations by discretizing a space of possible gantry-couchorientations, each point of a discretized space of possible gantry-couchorientations represents a corresponding gantry-couch orientation of theplurality of gantry-couch orientations.
 16. The radiation treatmentplanning system of claim 11, wherein in projecting the cost matrix oneach of a plurality of fluence planes, the one or more processors areconfigured to apply a weighted projection.
 17. The radiation treatmentplanning system of claim 16, wherein in applying the weightedprojection, the one or more processors are configured to weigh projectedvalues of the cost matrix according to a depth relative to a planningtarget volume (PTV) inside the anatomical region in a direction of aradiation beam.
 18. The radiation treatment planning system of claim 11,wherein in determining the sequence of gantry-couch orientations, theone or more processors are configured to: compute, for each gantry-couchorientation, a corresponding matrix sum value representing a sum of aprojection of the cost matrix on a target mask of a fluence planeassociated with the gantry-couch orientation; and determine the sequenceof gantry-couch orientations using matrix sum values computed for theplurality of gantry-couch orientations.
 19. The radiation treatmentplanning system of claim 18, wherein in determining the sequence ofgantry-couch orientations, the one or more processors are configured tominimize a total of matrix sum values over the treatment path, thetreatment path extending over a predefined range of gantry-couchorientations.
 20. A computer-readable medium including computer codeinstructions stored thereon, the computer code instructions whenexecuted cause one or more processors to: determine an estimate ofradiation dose distribution within an anatomical region of a patient;determine, using the estimate of radiation dose distribution, a costmatrix representing an objective function defined in terms of theestimate of radiation dose distribution and patient specific data;project the cost matrix on each of a plurality of fluence planes, eachof the plurality of fluence planes associated with a correspondinggantry-couch orientation of a plurality of gantry-couch orientations ofa medical linear accelerator device; and determine, using projections ofthe cost matrix on each of the plurality of fluence planes, a sequenceof gantry-couch orientations among the plurality of gantry-couchorientations representing a treatment path.